• DocumentCode
    2252614
  • Title

    The application of rough neural network in RMF model

  • Author

    Wang, Wei ; Mi, Hong

  • Author_Institution
    Dept of Autom., Xiamen Univ., Xiamen, China
  • Volume
    1
  • fYear
    2010
  • fDate
    6-7 March 2010
  • Firstpage
    210
  • Lastpage
    213
  • Abstract
    In many models of customer relationship management (CRM) analysis, RFM model is widely accepted. RMF model is an important tool to weigh customer value and customer profitability. To address this issue, this paper closely combines the rough set theory with neural network and uses rough set theory to process the random sample data from dataset. Then the data is projected from high-dimensional to low-dimensional, and the redundant attributes of sample data are removed. The sampling data which is processed after using rough set theory is trained on the neural network. At last, we use the test data to test and verify this model. Experimental results show that compared with the traditional BP neural network, rough neural network has a significant improvement in accuracy, and an advantage in the computing speed.
  • Keywords
    customer relationship management; neural nets; rough set theory; CRM; RMF model; customer profitability; customer relationship management; random sample data; rough neural network; rough set theory; weigh customer value; Asia; Customer relationship management; Data mining; Decision trees; Informatics; Neural networks; Robotics and automation; Set theory; Symmetric matrices; Testing; RMF model; attribute reduction; data mining; neural network; rough set;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Informatics in Control, Automation and Robotics (CAR), 2010 2nd International Asia Conference on
  • Conference_Location
    Wuhan
  • ISSN
    1948-3414
  • Print_ISBN
    978-1-4244-5192-0
  • Electronic_ISBN
    1948-3414
  • Type

    conf

  • DOI
    10.1109/CAR.2010.5456865
  • Filename
    5456865